Improving Sales Forecasting Using Real-time Monitoring of Out-of-Stock Events through deep learning based Object detection technique for Ice cream brands

Out-of-stock situations (OOS) are undesirable for brands especially in the ice cream business. Traditional prediction (forecasting) models which depend largely on historical sales data tend to fail while predicting optimal quantity of ice cream stock for various retailers. This is evident as the OOS situation is quite common especially in the summers. One of the reasons is that the models generally don’t use past OOS events to correct themselves. This is simply because gathering OOS data automatically is a challenge. Brands are dependent on the retailers to report the events as they occur who may forget to do so. Ice-cream cabinets are mostly placed horizontally with a number of baskets containing various flavors (SKUs).The key to identify OOS in ice cream cabinet is to determine the depth up to which an ice cream basket is filled with the SKU. We have devised an automated way to determine the OOS by leveraging our state-of-art deep learning based image recognition technology 35 hawk. 80% of OOS events avoided due to better stock predictions SKU Detection Accuracy: 95% Depth Detection Accuracy: 90% This approach is generic and can be extended to dairy, frozen foods which are kept in horizontal refrigerators (cabinets). In this presentation we intend to share our work and results with respect to the Indian market.

Author(s): Rohit Agarwal

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